Research advancements in the Use of artificial intelligence for prenatal diagnosis of neural tube defects DOI Creative Commons
Maryam Yeganegi,

Mahsa Danaei,

Sepideh Azizi

и другие.

Frontiers in Pediatrics, Год журнала: 2025, Номер 13

Опубликована: Апрель 17, 2025

Artificial Intelligence is revolutionizing prenatal diagnostics by enhancing the accuracy and efficiency of procedures. This review explores AI machine learning (ML) in early detection, prediction, assessment neural tube defects (NTDs) through ultrasound imaging. Recent studies highlight effectiveness techniques, such as convolutional networks (CNNs) support vector machines (SVMs), achieving detection rates up to 95% across various datasets, including fetal images, genetic data, maternal health records. SVM models have demonstrated 71.50% on training datasets 68.57% testing for NTD classification, while advanced deep (DL) methods report patient-level prediction 94.5% an area under receiver operating characteristic curve (AUROC) 99.3%. integration with genomic analysis has identified key biomarkers associated NTDs, Growth Associated Protein 43 (GAP43) Glial Fibrillary Acidic (GFAP), logistic regression 86.67% accuracy. Current AI-assisted technologies improved diagnostic accuracy, yielding sensitivity specificity 88.9% 98.0%, respectively, compared traditional 81.5% 92.2% specificity. systems also streamlined workflows, reducing median scan times from 19.7 min 11.4 min, allowing sonographers prioritize critical patient care. Advancements DL algorithms, Oct-U-Net PAICS, achieved recall precision 0.93 0.96, identifying abnormalities. Moreover, AI's evolving role research supports personalized prevention strategies enhances public awareness AI-generated messages. In conclusion, significantly improves leading greater As continues advance, it potential further enhance healthcare raise about ultimately contributing better outcomes.

Язык: Английский

Diagnostic accuracy of ultrasound in hyperthyroidism: A comprehensive review of recent studies DOI

Dawei Wang,

Chao Xie,

Xuena Zheng

и другие.

Journal of Radiation Research and Applied Sciences, Год журнала: 2025, Номер 18(2), С. 101370 - 101370

Опубликована: Фев. 26, 2025

Язык: Английский

Процитировано

0
Fetal origins of adult disease: transforming prenatal care by integrating Barker’s Hypothesis with AI-driven 4D ultrasound DOI
Wiku Andonotopo, Muhammad Adrianes Bachnas, Muhammad Ilham Aldika Akbar

и другие.

Journal of Perinatal Medicine, Год журнала: 2025, Номер unknown

Опубликована: Апрель 8, 2025

Abstract Introduction The fetal origins of adult disease, widely known as Barker’s Hypothesis, suggest that adverse environments significantly impact the risk developing chronic diseases, such diabetes and cardiovascular conditions, in adulthood. Recent advancements 4D ultrasound (4D US) artificial intelligence (AI) technologies offer a promising avenue for improving prenatal diagnostics validating this hypothesis. These innovations provide detailed insights into behavior neurodevelopment, linking early developmental markers to long-term health outcomes. Content This study synthesizes contemporary developments AI-enhanced US, focusing on their roles detecting anomalies, assessing neurodevelopmental markers, evaluating congenital heart defects. integration AI with US allows real-time, high-resolution visualization anatomy behavior, surpassing diagnostic precision traditional methods. Despite these advancements, challenges algorithmic bias, data diversity, real-world validation persist require further exploration. Summary Findings demonstrate AI-driven improves sensitivity accuracy, enabling earlier detection abnormalities optimization clinical workflows. By providing more comprehensive understanding programming, substantiate links between early-life conditions outcomes, proposed by Hypothesis. Outlook has potential revolutionize care, paving way personalized maternal-fetal healthcare. Future research should focus addressing current limitations, including ethical concerns accessibility challenges, promote equitable implementation. Such could reduce global burden diseases foster healthier generations.

Язык: Английский

Процитировано

0
Research advancements in the Use of artificial intelligence for prenatal diagnosis of neural tube defects DOI Creative Commons
Maryam Yeganegi,

Mahsa Danaei,

Sepideh Azizi

и другие.

Frontiers in Pediatrics, Год журнала: 2025, Номер 13

Опубликована: Апрель 17, 2025

Artificial Intelligence is revolutionizing prenatal diagnostics by enhancing the accuracy and efficiency of procedures. This review explores AI machine learning (ML) in early detection, prediction, assessment neural tube defects (NTDs) through ultrasound imaging. Recent studies highlight effectiveness techniques, such as convolutional networks (CNNs) support vector machines (SVMs), achieving detection rates up to 95% across various datasets, including fetal images, genetic data, maternal health records. SVM models have demonstrated 71.50% on training datasets 68.57% testing for NTD classification, while advanced deep (DL) methods report patient-level prediction 94.5% an area under receiver operating characteristic curve (AUROC) 99.3%. integration with genomic analysis has identified key biomarkers associated NTDs, Growth Associated Protein 43 (GAP43) Glial Fibrillary Acidic (GFAP), logistic regression 86.67% accuracy. Current AI-assisted technologies improved diagnostic accuracy, yielding sensitivity specificity 88.9% 98.0%, respectively, compared traditional 81.5% 92.2% specificity. systems also streamlined workflows, reducing median scan times from 19.7 min 11.4 min, allowing sonographers prioritize critical patient care. Advancements DL algorithms, Oct-U-Net PAICS, achieved recall precision 0.93 0.96, identifying abnormalities. Moreover, AI's evolving role research supports personalized prevention strategies enhances public awareness AI-generated messages. In conclusion, significantly improves leading greater As continues advance, it potential further enhance healthcare raise about ultimately contributing better outcomes.

Язык: Английский

Процитировано

0